Abstract
We present a new approach to quantify the co-deletion of chromosomal arms 1p/19q status in lower grade glioma (LGG). Though the surgical biopsy followed by fluorescence in-situ hybridization test is the gold standard currently to identify mutational status for diagnosis and treatment planning, there are several imaging studies to predict the same. Our study aims to determine the 1p/19q mutational status of LGG non-invasively by advanced pattern analysis using multi-parametric MRI. The publicly available dataset at TCIA was used. T1-W and T2-W MRIs of a total 159 patients with grade-II and grade-III glioma, who had biopsy proven 1p/19q status consisting either no deletion (n = 57) or co-deletion (n = 102), were used in our study. We quantified the imaging profile of these tumors by extracting diverse imaging features, including the tumor’s spatial distribution pattern, volumetric, texture, and intensity distribution measures. We integrated these diverse features via support vector machines, to construct an imaging signature of 1p/19q, which was evaluated in independent discovery (n = 85) and validation (n = 74) cohorts, and compared with the 1p/19q status obtained through fluorescence in-situ hybridization test. The classification accuracy on complete, discovery and replication cohorts was 86.16%, 88.24%, and 85.14%, respectively. The classification accuracy when the model developed on training cohort was applied on unseen replication set was 82.43%. Non-invasive prediction of 1p/19q status from MRIs would allow improved treatment planning for LGG patients without the need of surgical biopsies and would also help in potentially monitoring the dynamic mutation changes during the course of the treatment.
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References
Louis, D., et al.: World Health Organization classification of tumours of the central nervous system. In: International Agency for Research on Cancer, Lyon, p. 4 (2007)
Fellah, S., et al.: Multimodal MR imaging (diffusion, perfusion, and spectroscopy): is it possible to distinguish oligodendroglial tumor grade and 1p/19q codeletion in the pretherapeutic diagnosis? AJNR Am. J. Neuroradiol. 34, 1326–1333 (2013)
Jansen, N.L., et al.: Prediction of oligodendroglial histology and LOH 1p/19q using dynamic [(18)F]FET-PET imaging in intracranial WHO grade II and III gliomas. Neuro Oncol. 14, 1473–1480 (2012)
Iwadate, Y., et al.: Molecular imaging of 1p/19q deletion in oligodendroglial tumours with 11C-methionine positron emission tomography. J. Neurol. Neurosurg. Psychiatry 87, 1016–1021 (2016)
Bourdillon, P., et al.: Prediction of anaplastic transformation in low-grade oligodendrogliomas based on magnetic resonance spectroscopy and 1p/19q codeletion status. J. Neurooncol. 122, 529–537 (2015)
Akkus, Z., et al.: Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J. Digit. Imaging 30, 469–476 (2017)
Zhou, H., et al.: MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol. 19, 862–870 (2017)
Chaddad, A., et al.: Predicting the gene status and survival outcome of lower grade glioma patients with multimodal MRI features. IEEE Access 7, 75976–75984 (2019)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011)
Rathore, S., et al.: Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1. Sci. Rep. 8, 5087 (2018)
Rathore, S., et al.: A radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning. J. Med. Imaging 5, 021219 (2018)
Macyszyn, L., et al.: Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol. 18, 417–425 (2016)
Rathore, S., et al.: Technical note: a radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma. In: Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10576, p. 105760O (2018)
Shukla-Dave, A., et al.: The utility of magnetic resonance imaging and spectroscopy for predicting insignificant prostate cancer: an initial analysis. BJU Int. 99, 786–793 (2007)
Rathore, S., et al.: Multivariate pattern analysis of de novo glioblastoma patients offers in vivo evaluation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status, compensating for insufficient specimen and assay failures. J. Neuro-oncol. 20, vi186 (2018)
Bakas, S., et al.: In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the phi-index. Clin. Cancer Res.: Off. J. Am. Assoc. Cancer Res. 23, 4724–4734 (2017)
Smith, S.M., Brady, J.M.: SUSAN - a new approach to low level image processing. Int. J. Comput. Vis. 23, 45–78 (1997)
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001)
Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002)
Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31, 1116–1128 (2006)
Haralick, R.M., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
Galloway, M.M.: Texture analysis using grey level run lengths. Comput. Graph. Image Process. 4, 172–179 (1975)
Bilello, M., et al.: Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma. Neuroimage Clin. 12, 34–40 (2016)
Rathore, S., et al.: GECC: gene expression based ensemble classification of colon samples. IEEE/ACM Trans. Comput. Biol. Bioinf. 11, 1131–1145 (2014)
Rathore, S., et al.: Automated colon cancer detection using hybrid of novel geometric features and some traditional features. Comput. Biol. Med. 65, 279–296 (2015)
Sullivan, G.M., Feinn, R.: Using effect size-or why the P value is not enough. J. Grad. Med. Educ. 4, 279–282 (2012)
Verhaak, R.G., et al.: Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010)
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Rathore, S., Chaddad, A., Bukhari, N.H., Niazi, T. (2020). Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma. In: Mohy-ud-Din, H., Rathore, S. (eds) Radiomics and Radiogenomics in Neuro-oncology. RNO-AI 2019. Lecture Notes in Computer Science(), vol 11991. Springer, Cham. https://doi.org/10.1007/978-3-030-40124-5_7
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DOI: https://doi.org/10.1007/978-3-030-40124-5_7
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